Skip to Main Navigation

Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country (English)

Obtaining consistent estimates on poverty over time as well as monitoring poverty trends on a timely basis is a priority concern for policy makers. However, these objectives are not readily achieved in practice when household consumption data are neither frequently collected, nor constructed using consistent and transparent criteria. This paper develops a formal framework for survey-to-survey poverty imputation in an attempt to overcome these obstacles, and to elevate the discussion of these methods beyond the largely ad-hoc efforts in the existing literature. The framework introduced here imposes few restrictive assumptions, works with simple variance formulas, provides guidance on the selection of control variables for model building, and can be generally applied to imputation either from one survey to another survey with the same design, or to another survey with a different design. Empirical results analyzing the Household Expenditure and Income Survey and the Unemployment and Employment Survey in Jordan are quite encouraging, with imputation-based poverty estimates closely tracking the direct estimates of poverty.


  • Author

    Dang,Hai-Anh H., Lanjouw,Peter F., Serajuddin,Umar

  • Document Date


  • Document Type

    Policy Research Working Paper

  • Report Number


  • Volume No


  • Total Volume(s)


  • Country



  • Region

    Middle East and North Africa,

  • Disclosure Date


  • Disclosure Status


  • Doc Name

    Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country

  • Keywords

    consumption datum;household consumption;source of drinking water;change in poverty;poverty estimate;missing data;household expenditure survey data;millennium development goal;labor force survey;imputation method;estimates of poverty;random effects model;parameters in equation;per capita income;high poverty rate;development research group;linear regression model;estimation result;official poverty line;census enumeration area;female household member;number of refugees;Demographic and Health;change in consumption;estimation of poverty;nationally representative survey;list of asset;consumer price index;cost of living;headcount poverty rate;years of schooling;official poverty rate;living in poverty;household survey data;high energy price;Economic Studies;economic study;poverty trend;statistical studies;normal distribution;household asset;household characteristic;population group;confidence interval;survey design;explanatory variable;estimation procedure;administrative level;economics literature;consumption survey;multiple imputation;consumption aggregate;household head;unobserved variable;sampling frame;consumption distribution;real gdp;standard error;decomposition results;Cash Transfer;sampling design;panel data;positive correlation;Macroeconomic Trends;linear projection;model specification;matching method;official estimates;empirical result;estimation technique;consumption level;clean stones;literature review;poverty debate;econometric model;sampling method;measurement error;sampling units;econometric issue;statistical software;field work;cross sections;solar boiler;common feature;sewing machine;energy source;construction material;fax machine;poverty change;age range;parsimonious model;household size;television set;physical characteristic;urban residence;household demographics;point estimate;upper bind;test procedure;household wealth;war refugees;vacuum cleaner;poverty decline;spring water;economic stress;Labor Market;asset index;water filter;air conditioner;marginal effect;small fraction;washing machine;asset variable;policy work;poverty eradication;measuring poverty;extreme poverty;international community;global poverty;statistical term;research assistance;employment rate;statistical package;agricultural wage;household data;commodity price;empirical application;regional characteristic;population census;consumption parameter;Elderly People;welfare function;consumption model;net change;household variables;average household;regime change;poverty datum;statistical theory;poverty comparison;parameter estimate;implicit assumption;data availability;escape poverty;technical expertise;economic reform;comprehensive treatment;interesting case;standardization procedure;geographical coverage;theoretical front;empirical front;data requirement;political science;development policy;open access;monitoring poverty;consistent estimate;financial resource;macroeconomic data;reasonable estimate;cluster sampling;poverty status;consumption habit;regression results;ownership rate;Water Network;functional form;accurate estimate;sampling error;consumption change;luxury good;rain water;ppp terms;mineral water;poor household;consumption pattern;water tank;



Official version of document (may contain signatures, etc)

  • Official PDF
  • TXT*
  • Total Downloads** :
  • Download Stats
  • *The text version is uncorrected OCR text and is included solely to benefit users with slow connectivity.


Dang,Hai-Anh H. Lanjouw,Peter F. Serajuddin,Umar

Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country (English). Policy Research working paper,no. WPS 7043,Paper is funded by the Strategic Research Program (SRP),LSMS Washington, D.C. : World Bank Group.